SoroushVahidi/scidocs huggingface dataset:
Health data integration with Secured Record Linkage: A practical solution for Bangladesh and other developing countries
(2017)

Knowledge discovery from various health data repositories requires the incorporation of healthcare data from diversified sources. Maintaining record linkage during the integration of medical data is an important research issue. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), universal patient identifier (UPI), health insurance number, etc. These solutions cannot be used correctly for record linkage of health data of developing countries because of missing data, ambiguity in patient identification, and high amount of noise in patient information. Also, identifiable health data in electronic health repositories may produce a significant risk to patient privacy and also make the health information systems security vulnerable to hackers. In this paper, we have analyzed the practical problems of collecting and integrating healthcare data in Bangladesh for developing national health data warehouse. We have proposed a privacy preserved secured record linkage architecture that can support constrained health data of developing countries such as Bangladesh. Our technique can anonymize identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. Experimental results show that our proposed method successfully linked records with acceptable accuracy for noisy data in the absence of any standard ID like SSN.
PEIR, the personal environmental impact report, as a platform for participatory sensing systems research
(2009)

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.
Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters
(2009)

The enormous growth of data in a variety of applications has increased the need for high performance data mining based on distributed environments. However, standard data mining toolkits per se do not allow the usage of computing clusters. The success of MapReduce for analyzing large data has raised a general interest in applying this model to other, data intensive applications. Unfortunately current research has not lead to an integration of GUI based data mining toolkits with distributed file system based MapReduce systems. This paper defines novel principles for modeling and design of the user interface, the storage model and the computational model necessary for the integration of such systems. Additionally, it introduces a novel system architecture for interactive GUI based data mining of large data on clusters based on MapReduce that overcomes the limitations of data mining toolkits. As an empirical demonstration we show an implementation based on Weka and Hadoop.
Robust Distributed Spectrum Sensing in Cognitive Radio Networks
(2008)

Distributed spectrum sensing (DSS) enables a Cognitive Radio (CR) network to reliably detect licensed users and avoid causing interference to licensed communications. The data fusion technique is a key component of DSS. We discuss the Byzantine failure problem in the context of data fusion, which may be caused by either malfunctioning sensing terminals or Spectrum Sensing Data Falsification (SSDF) attacks. In either case, incorrect spectrum sensing data will be reported to a data collector which can lead to the distortion of data fusion outputs. We investigate various data fusion techniques, focusing on their robustness against Byzantine failures. In contrast to existing data fusion techniques that use a fixed number of samples, we propose a new technique that uses a variable number of samples. The proposed technique, which we call Weighted Sequential Probability Ratio Test (WSPRT), introduces a reputation-based mechanism to the Sequential Probability Ratio Test (SPRT). We evaluate WSPRT by comparing it with a variety of data fusion techniques under various network operating conditions. Our simulation results indicate that WSPRT is the most robust against the Byzantine failure problem among the data fusion techniques that were considered.
A Local Detection Approach for Named Entity Recognition and Mention Detection
(2017)

In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, we propose a new local detection approach, which relies on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixedsize representation. Subsequently, a simple feedforward neural network (FFNN) is learned to either reject or predict entity label for each individual text fragment. The proposed method has been evaluated in several popular NER and MD tasks, including CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Tri-lingual Entity Discovery and Linking (EDL) tasks. Our method has yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labeling methods.
Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models
(2015)

A commonly used paradigm in diverse application areas is to assume that an observed set of individual binary features is generated from a Bernoulli distribution with probabilities varying according to a Beta distribution. In this paper, we present our nonparametric variational inference algorithm for the Beta-Bernoulli observation model. Our primary focus is clustering discrete binary data using the Dirichlet process (DP) mixture model.
Inverted 'V--Y' advancement medial epicanthoplasty.
(2012)

The presence of an epicanthal fold is a distinctive anatomic feature of the Asian population. Epicanthoplasty can be very helpful in resolving the above problematic anatomy and may produce a more aesthetic appearance. The goal of this report is to describe a modification in the traditional operative methods related to medial epicanthoplasty, to minimise scar appearance and maximise normal appearance. An inverse triangular flap of skin is based on the semilunar skin, which is elevated and retracted within the line of the normal eyelid. The design of four key points is crucial to this epicanthoplasty. A total of 62 patients were performed with this method; 42 of this group underwent simultaneous double-eyelid plasty. Most of the patients obtained satisfactory results aesthetically followed from 6 months to 4 years postoperation. There was no recurrence of the epicanthal fold. As many as 38 patients were followed by interview and photographs were taken at the same time. The rest of the patients were followed up through telephone. No patients complained about visible scarring in the epicanthal area. As many as 60 patients obtained satisfactory results aesthetically, two patients complained about the median canthal asymmetry. The result indicates the reliability and feasibility of epicanthoplasty.
Operator Splitting Methods in Compressive Sensing and Sparse Approximation
(2016)

Compressive sensing and sparse approximation have many emerging applications, and are a relatively new driving force for development of splitting methods in optimization. Many sparse coding problems are well described by variational models with `1-norm penalties and constraints that are designed to promote sparsity. Successful algorithms need to take advantage of the separable structure of potentially complicated objectives by “splitting” them into simpler pieces and iteratively solving a sequence of simpler convex minimization problems. In particular, isolating `1 terms from the rest of the objective leads to simple soft thresholding updates or `1 ball projections. A few basic splitting techniques can be used to design a huge variety of relevant algorithms. This chapter will focus on operator splitting strategies that are based on proximal operators, duality, and alternating direction methods. These will be explained in the context of basis pursuit variants and through compressive sensing applications.
On the bipolarity in argumentation frameworks
(2004)

In this paper, we propose a survey of the use of bipolarity in argumentation frameworks, i.e. the presence of two kinds of entities (a positive entity and a negative entity). An argumentation process follows three steps: building the arguments and the interactions between them, valuating the arguments using or not the interactions and finally defining the acceptability of the arguments. This paper shows on various applications and with some formal definitions that bipolarity appears (in some cases since always) and can be used in each step of this process under different forms.
MULTI-SKILLED MOTION CONTROL
(2018)

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.
A gesture-driven computer interface using Kinect
(2012)

Automatic recognition of human actions from video has been studied for many years. Although still very difficult in uncontrolled scenarios, it has been successful in more restricted settings (e.g., fixed viewpoint, no occlusions) with recognition rates approaching 100%. However, the best-performing methods are complex and computationally-demanding and thus not well-suited for real-time deployments. This paper proposes to leverage the Kinect camera for close-range gesture recognition using two methods. Both methods use feature vectors that are derived from the skeleton model provided by the Kinect SDK in real-time. Although both methods perform nearest-neighbor classification, one method does this in the space of features using the Euclidean distance metric, while the other method does this in the space of feature covariances using a log-Euclidean metric. Both methods recognize 8 hand gestures in real time achieving correct-classification rates of over 99% on a dataset of 20 subjects but the method based on Euclidean distance requires feature-vector collections to be of the same size, is sensitive to temporal misalignment, and has higher computation and storage requirements.
Orchestrating a mixed reality performance
(2001)

A study of a professional touring mixed reality performance called Desert Rain yields insights into how performers orchestrate players' engagement in an interactive experience. Six players at a time journey through an extended physical and virtual set. Each sees a virtual world projected onto a screen made from a fine water spray. This acts as a traversable interface, supporting the illusion that performers physically pass between real and virtual worlds. Live and video-based observations of Desert Rain, coupled with interviews with players and the production team, have revealed how the performers create conditions for the willing suspension of disbelief, and how they monitor and intervene in the players experience without breaking their engagement. This involves carefully timed performances and “off-face” and “virtual” interventions. In turn, these are supported by the ability to monitor players' physical and virtual activity through asymmetric interfaces.
History of Computers, Electronic Commerce, and Agile Methods
(2008)

The purpose of this document is to present a literature review relevant to a study of using agile methods to manage the development of Internet websites and their subsequent quality. This study places website quality within the context of the $2 trillion U.S. ecommerce industry. Thus, this section provides a history of electronic computers, electronic commerce, software methods, software quality metrics, agile methods, and studies of agile methods. None of these histories are without controversy. For instance, some scholars begin the study of the electronic computer by mentioning the emergence of the Sumerian text, Hammurabi code, or the Abacus. We, however, will align our history with the emergence of the modern electronic computer at the beginning of World War II. The history of electronic commerce also has poorly defined beginnings. Some studies of electronic commerce begin with the widespread use of the Internet in the early 1990s. However, electronic commerce cannot be appreciated without establishing a deeper context. The origins of the software industry are also somewhat poorly documented. It started sometime in the 1960s, though it didn't really take off until the 1980s. Software methods, however, are often associated with the rise of structured design in 1969, though they can firmly be traced back to the late 1950s. The first uses of agile methods are also not well defined. Many associate agile methods with the appearance of extreme programming in 1999, though agile methods began taking hold in the early 1990s, and most of its principles appeared before 1975. While research on agile methods began appearing in 1998, therein lies the issue. Few scholarly studies, if any, have been performed on agile methods, which is the basic purpose of this literature review. That is, to establish the context to conduct scholarly research within the fields of agile methods and electronic commerce. Since agile methods may be linked to outcomes such as software quality, an in-depth analysis of literature on software quality metrics is also included.History of Computers and Software Figure 1. Timeline and history of computers and software Page-3-Electronic computers. Electronic computers are simply machines that perform useful functions such as mathematical calculations or inputting, processing, and outputting data and information in meaningful forms (Rosen, 1969). Modern electronic computers are characterized by four major generations: first generation vacuum tube
Secure file storage in cloud computing using hybrid cryptography algorithm
(2016)

Now a day's cloud computing is used in many areas like industry, military colleges etc to storing huge amount of data. We can retrieve data from cloud on request of user. To store data on cloud we have to face many issues. To provide the solution to these issues there are n number of ways. Cryptography and steganography techniques are more popular now a day's for data security. Use of a single algorithm is not effective for high level security to data in cloud computing. In this paper we have introduced new security mechanism using symmetric key cryptography algorithm and steganography. In this proposed system AES, blowfish, RC6 and BRA algorithms are used to provide block wise security to data. All algorithm key size is 128 bit. LSB steganography technique is introduced for key information security. Key information contains which part of file is encrypted using by which algorithm and key. File is splited into eight parts. Each and every part of file is encrypted using different algorithm. All parts of file are encrypted simultaneously with the help of multithreading technique. Data encryption Keys are inserted into cover image using LSB technique. Stego image is send to valid receiver using email. For file decryption purpose reverse process of encryption is applied.
A 4K-Input High-Speed Winner-Take-All (WTA) Circuit with Single-Winner Selection for Change-Driven Vision Sensors
(2019)

Winner-Take-All (WTA) circuits play an important role in applications where a single element must be selected according to its relevance. They have been successfully applied in neural networks and vision sensors. These applications usually require a large number of inputs for the WTA circuit, especially for vision applications where thousands to millions of pixels may compete to be selected. WTA circuits usually exhibit poor response-time scaling with the number of competitors, and most of the current WTA implementations are designed to work with less than 100 inputs. Another problem related to the large number of inputs is the difficulty to select just one winner, since many competitors may have differences below the WTA resolution. In this paper, a WTA circuit is presented that handles more than four thousand inputs, to our best knowledge the hitherto largest WTA, with response times below the microsecond, and with a guaranty of just a single winner selection. This performance is obtained by the combination of a standard analog WTA circuit and a fast digital single-winner selector with almost no size penalty. This WTA circuit has been successfully employed in the fabrication of a Selective Change-Driven Vision Sensor based on 180 nm CMOS technology. Both simulated and experimental results are presented in the paper, showing that a single pixel event can be selected in just 560 ns, and a multipixel pixel event can be processed in 100 us. Similar results with a conventional approach would require a camera working at more than 1 Mfps for the single-pixel event detection, and 10 kfps for the whole multipixel event to be processed.
Monte Carlo Localization for Mobile Robots
(1999)

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular, the problems encountered are closely related to the type of representation used to represent probability densities over the robot’s state space. Recent work on Bayesian filtering with particle-based density representations opens up a new approach for mobile robot localization, based on these principles. In this paper we introduce the Monte Carlo Localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods.
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
(1999)

This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation “where needed.” The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement.
A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking
(2002)

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or “particle”) representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.
PowerMatcher: multiagent control in the electricity infrastructure
(2005)

Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation.
Investigating performance of students: a longitudinal study
(2015)

This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.